PROJECT SUMMARY The number of people with diabetes worldwide is predicted to increase from 415 million in 2015 to 642 million in 2040. The health care costs of treating the disease account for 12% of the global health expenditure and continue to be a huge economic burden (IDF World Atlas 2015). Type 1 diabetes mellitus (T1DM) constitutes 10-15% of the disease burden and is an autoimmune disease, thought to be triggered by genetic or environmental factors in early childhood, which results in antibody-mediated destruction of insulin producing pancreatic ?-cells causing life-threatening hyperglycemia. In contrast, type 2 diabetes mellitus (T2DM) develops later in life and results from insulin resistance in target tissues and inflammation coupled with an inadequate compensation by the ?-cells. The precise mechanisms that trigger the cascade of events ultimately leading to beta cell death are still not fully understood. Several observations have prompted renewed interest in the beta cell itself as a target tissue that could initiate the disease process. In this context the recent identification of RNA methylation as a potential regulatory mechanism that contributes to the ability of a cell to adapt to rapid changes in the environment provides a provocative approach to investigate changes in beta cells that precede the development of the type 1 diabetes phenotype. We seek to interrogate alterations in the dynamic methylation of RNA, specifically, N6-adenosine methylation (m6A) in beta cells to identify the signatures that might provide important clues to processes that contribute to beta cell death. In this proposal we will: 1). Characterize the dynamic RNA methylation changes in islet cells obtained from models of type 1 diabetes; 2) determine the functional relevance of alterations in N6-methyladenosine (m6A) in human islets and ?-cells; and 3) explore changes in additional methyl marks in RNA in islet cells from models of T1D mouse model. Finally, we will contrast the RNA methylation changes observed in the islets in the T1D models with proteomics and gene expression signatures in islets obtained from patients with T1D.